With the growing data privacy concern, many person ReID systems are changing to the decentralized or federated training paradigm, where the parameters for edge models can be jointly updated among different source domains instead of centralising private data for training.
One challenge for federated person ReID is knowledge interference due to the data heterogeneity across different source
domains.
To address this problem, FedReID [1]} and some other works [2]}, [3]} adopted model aggregation and knowledge distillation to optimize the performances for the federated person ReID.
SKA [2]} proposed a selective knowledge aggregation method to transfer personalized knowledge among different edge clients.
However, most of the existing works for federated person ReID ignored the spatial-temporal correlation for domain knowledge from different edge locations and moments, which limits the efficiency of knowledge sharing.
Our method can capture task-specific knowledge by integrating spatial-temporal knowledge from the edge clients to improve performances.
